基于深度学习的相位解缠方法  

Phase Unwrapping Method Based on Deep Learning

作　　者：田宪辉 谢先明 刘媛媛 李春 曾庆宁[1] TIAN Xianhui;XIE Xianming;LIU Yuanyuan;LI Chun;ZENG Qingning(School of Information and Communication Engineering,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China;School of Electrical and Information Engineering,Guangxi University of Science and Technology,Liuzhou,Guangxi 545006,China)

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541004 [2]广西科技大学电气与信息工程学院,广西柳州545006

出　　处：《遥感信息》2022年第5期93-100,共8页Remote Sensing Information

基　　金：国家自然科学基金项目(62161003);桂林电子科技大学教育部重点实验室基金项目(CRK170108);广西无线宽带通信与信号处理重点实验室基金项目(GXKL06180102);2020年桂林电子科技大学研究生科研创新项目(2020YCXS020)。

摘　　要：针对干涉测量技术应用中的缠绕相位解缠问题,提出了一种基于U-Net3+的深度学习相位解缠方法。该方法以U-Net3+为架构,首先,利用全尺度的跳跃连接(skip connection)整合来自不同尺度特征图中的高级语义与低级语义信息;其次,利用深度监督加快网络收敛速度,同时增强干涉图特征信息;为防止梯度爆炸,在网络中添加残差网络,最终搭建适用于不同条纹类型干涉图解缠的网络架构,建立从缠绕相位到真实相位的直接映射关系。完成训练后的网络可直接对缠绕干涉图进行相位解缠以获得其真实相位估计,并在不同条纹类型的干涉图相位解缠实验中均表现出良好的稳定性。Aiming at the problem of phase unwrapping for interferograms in interferometry,a phase unwrapping method based on U-Net3+ is proposed to retrieve the unwrapped phase from the wrapped phase.This method uses U-Net3+ as the skeleton,firstly integrates high-level and low-level semantic information from feature graphs at different scales by using full-scale skip connections.Secondly,deep supervision is used to accelerate the convergence speed of the network and enhance the characteristic information of the interferograms.Finally,the residual network is added to the network to prevent gradient explosion,and a network architecture suitable for unwrapping different types of fringe patterns is constructed to realize the direct mapping from the wrapped phase to the true unwrapped phase.The trained network can obtain the unwrapped phase from the wrapped phase,and shows good robustness in the experiments of phase unwrapping for different types of fringe patterns.

关 键 词：相位解缠 U-Net3+ 残差网络 深度学习 干涉图 

分 类 号：P236[天文地球—摄影测量与遥感]